京津冀区域电离层TEC多参数融合预测模型

doi:10.12196/j.issn.1000-3274.2024.02.002

摘要/Abstract

摘要： 本文利用中国大陆构造环境监测网络(以下简称“陆态网络”)GPS站点实测总电子含量(total electron content, TEC)数据, 建立了京津冀区域电离层经验模型, 并通过引入太阳通量和地磁活动数据来提高模型的性能。本文研究了电离层TEC的日变化、季节变化和地磁影响分量的函数模型, 采用非线性最小二乘法拟合待定系数, 提出了一种多参数经验融合模型电离层TEC京津冀区域模型(Multi-Parameter Empirical Fusion Model-Ionospheric TEC Beijing-Tianjin-Hebei Region, MEFM-ITBTHR)来预测京津冀区域电离层TEC。结果表明, MEFM-ITBTHR模型能够很好地拟合建模数据集。本文还对MEFM-ITBTHR模型的性能进行了地理位置变化分析、季节变化分析和地磁扰动分析。结果说明在京津冀区域, MEFM-ITBTHR模型在不同经纬度、不同季节、不同地磁扰动下与实测TEC的预测效果、线性相关性、模型精度都优于IRI2020和NeQuick2模型。本文构建的区域TEC经验模型可为GNSS单频用户提供新的电离层延迟改正方法, 同时对建立其他新的和改进现有的电离层经验模型具有重要的参考意义。

关键词: 经验模型, IRI2020, NeQuick2, TEC

Abstract: This article utilizes the Total Electron Content (TEC) data measured by the GPS stations of the Chinese Mainland Crustal Movement Observation Network (referred to as the “Crustal Network” hereafter) to establish an empirical ionospheric model for the Beijing-Tianjin-Hebei region. By incorporating solar flux and geomagnetic activity data, the performance of the model is enhanced. The study develops a functional model for the diurnal, seasonal variation, and geomagnetic effect components of the ionospheric TEC, using a nonlinear least squares method to fit the coefficients. A multi-parameter empirical fusion model is proposed-the Ionospheric TEC Beijing-Tianjin-Hebei Region Model (MEFM-ITBTHR) - to predict the ionospheric TEC in the Beijing-Tianjin-Hebei region. Results indicate that the MEFM-ITBTHR model fits the modeling dataset well. The performance of the MEFM-ITBTHR model is further analyzed through geographical variation, seasonal variation, and geomagnetic disturbance analysis. Results demonstrate that in the Beijing-Tianjin-Hebei region, the MEFM-ITBTHR model exhibits better forecasting accuracy, linear correlation, and model precision for measured TEC across different latitudes, seasons, and geomagnetic disturbances compared to the IRI2020 and NeQuick2 models. The regional TEC empirical model constructed in this study provides a new method for ionospheric delay correction for GNSS single-frequency users and holds significant reference value for establishing other new and improving existing empirical ionospheric models.

Key words: Empirical model, IRI2020, NeQuick2, TEC

中图分类号:

P228.4

陈江河, 熊攀, 武浩琛, 王树凯. 京津冀区域电离层TEC多参数融合预测模型[J]. 地震, 2024, 44(2): 12-32.

CHEN Jiang-he, XIONG Pan, WU Hao-chen, WANG Shu-kai. A Multi-Parameter Fusion Forecast Model of Ionospheric TEC in the Beijing-Tianjin-Hebei Region[J]. EARTHQUAKE, 2024, 44(2): 12-32.

参考文献

[1] Hochegger G, Nava B, Radicella S, et al. A family of ionospheric models for different uses[J]. Physics and Chemistry of the Earth, Part C: Solar, Terrestrial & Planetary Science, 2000, 25(4): 307-310.
[2] Klobuchar J A. Ionospheric time-delay algorithm for single-frequency GPS users[J]. IEEE Transactions on Aerospace and Electronic Systems, 1987, AES-23(3): 325-331.
[3] 姜卫平, 邹璇, 唐卫明. 基于CORS网络的单频GPS实时精密单点定位新方法[J]. 地球物理学报, 2012, 55(5): 1549-1556.
JIANG Wei-ping, ZOU Xuan, TANG Wei-ming. A new kind of real-time PPP method for GPS single-frequency receiver using CORS network[J]. Chinese Journal of Geophysics, 2012, 55(5): 1549-1556 (in Chinese).
[4] 张瑞. 多模GNSS实时电离层精化建模及其应用研究[D]. 武汉: 武汉大学, 2013.
ZHANG Rui. Theory and method on multimode GNSS real-time refinement ionospheric modeling and its application[D]. Wuhan: Wuhan University, 2013 (in Chinese).
[5] Yuen P C, Roelofs T H. Seasonal variations in ionospheric total electron content[J]. Journal of Atmospheric and Terrestrial Physics, 1967, 29(3): 321-326.
[6] 余涛, 万卫星, 刘立波, 等. 利用IGS数据分析全球TEC的周年和半年变化特性[J]. 地球物理学报, 2006, 49(4): 943-949.
YU Tao, WAN Wei-xing, LIU Li-bo, et al. Using IGS data to analysis the global TEC annual and semiannual variation[J]. Chinese Journal of Geophysics-Chinese Edition, 2006, 49(4): 943-949 (in Chinese).
[7] Zhao B, Wan W, Liu L, et al. Features of annual and semiannual variations derived from the global ionospheric maps of total electron content[J]. Annales Geophysicae, 2007, 25(12): 2513-2527.
[8] Bagiya M S, Joshi H P, Iyer K N, et al. TEC variations during low solar activity period (2005—2007) near the equatorial ionospheric anomaly crest region in India[J]. Annales Geophysicae, 2009, 27(3): 1047-1057.
[9] 冯建迪, 王正涛, 赵珍珍. 卫星导航服务的全球电离层时变特性分析[J]. 测绘科学, 2015, 40(2): 13-17.
FENG Jian-di, WANG Zheng-tao, ZHAO Zhen-zhen. Analysis of temporal variation of global ionosphere based on IGS[J]. Science of Surveying and Mapping, 2015, 40(2): 13-17 (in Chinese).
[10] 冯建迪, 王正涛, 时爽爽, 等. 总电子含量赤道异常变化特性分析[J]. 测绘科学, 2016, 41(6): 44-47+52.
FENG Jian-di, WANG Zheng-tao, SHI Shuang-shuang, et al. Using IGS to analyze the variation of anomaly equatorial ionization[J]. Science of Surveying and Mapping 2016, 41(6): 44-47+52 (in Chinese).
[11] Liu J, Hernandez-Pajares M, Liang X, et al. Temporal and spatial variations of global ionospheric total electron content under various solar conditions[J]. Journal of Geodesy, 2017, 91: 485-502.
[12] 李涌涛, 李建文, 魏绒绒, 等. 全球电离层TEC格网时空变化特性分析[J]. 武汉大学学报(信息科学版), 2020, 45(5): 776-783.
LI Yong-tao, LI Jian-wen, WEI Rong-rong, et al. Analysis of temporal and spatial variation characteristics of global ionospheric TEC grid[J]. Geomatics and Information Science of Wuhan University, 2020, 45(5): 776-783 (in Chinese).
[13] 张学民, 申旭辉, 赵庶凡, 等. 地震电离层探测技术及其应用研究进展[J]. 地震学报, 2016, 38(3): 356-375.
ZHANG Xue-min, SHEN Xu-hui, ZHAO Shu-fan, et al. The seismo-ionospheric monitoring technologies and their application research development[J]. Acta Seismologica Sinica, 2016, 38(3): 356-375 (in Chinese).
[14] Shen X H, Zong Q G, Zhang X M. Introduction to special section on the China Seismo-Electromagnetic Satellite and initial results[J]. Earth and Planetary Physics, 2018, 2(6): 439-443.
[15] 张学民, 申旭辉, 钱家栋, 等. 我国地震电磁卫星数据分析及应用研究进展[J]. 地震, 2009, 29(S1): 34-45.
ZHANG Xue-min, SHEN Xu-hui, QIAN Jia-dong, et al. Advances in the analysis and application of seismo-electromagnetic satellite data in China[J]. Earthquake, 2009, 29(S1): 34-45 (in Chinese).
[16] Liu C Y, Liu J Y, Chen Y I, et al. Statistical analyses on the ionospheric total electron content related to M≥6.0 earthquakes in China during 1998—2015[J]. Terrestrial Atmospheric and Oceanic Sciences, 2018, 29(5): 485-498.
[17] Xu T, Hu Y L, Wu J, et al. Statistical analysis of seismo-ionospheric perturbation before 14 M_S≥7.0 strong earthquakes in Chinese subcontinent[J]. Chinese Journal of Radio Science, 2012, 27(3): 507-512.
[18] Feng J, Yuan Y, Zhang T, et al. Analysis of ionospheric anomalies before the Tonga volcanic eruption on 15 January 2022[J]. Remote Sensing, 2023, 15(19): 4879.
[19] Xiong P, Long C, Zhou H, et al. Pre-earthquake ionospheric perturbation identification using CSES data via transfer learning[J]. Frontiers in Environmental Science, 2021, 9: 779225.
[20] Xiong P, Long C, Zhou H, et al. Identification of electromagnetic pre-earthquake perturbations from the DEMETER data by machine learning[J]. Remote Sensing, 2020, 12(21): 3643.
[21] Xiong P, Long C, Zhou H, et al. GNSS TEC-based earthquake ionospheric perturbation detection using a novel deep learning framework[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 4248-4263.
[22] Xiong P, Marchetti D, De Santis A, et al. SafeNet: SwArm for earthquake perturbations identification using deep learning networks[J]. Remote Sensing, 2021, 13(24): 5033.
[23] Heki K. Ionospheric electron enhancement preceding the 2011 Tohoku-Oki earthquake[J]. Geophysical Research Letters, 2011, 38(17): L17312.
[24] Heki K, Enomoto Y. M_W dependence of the preseismic ionospheric electron enhancements[J]. Journal of Geophysical Research: Space Physics, 2015, 120(8): 7006-7020.
[25] Zhang X, Liu J, De Santis A, et al. Lithosphere-atmosphere-ionosphere coupling associated with four Yutian earthquakes in China from GPS TEC and electromagnetic observations onboard satellites[J]. Journal of Geodynamics, 2023, 155: 101943.
[26] 毛田, 万卫星, 刘立波. 用经验正交函数构造武汉地区电子浓度总含量的经验模式[J]. 地球物理学报, 2005, 48(4): 751-758.
MAO Tian, WAN Wei-xing, LIU Li-bo. An EOF-based empirical model of TEC over Wuhan[J]. Chinese Journal of Geophysics, 2005, 48(4): 751-758 (in Chinese).
[27] Huang Z, Yuan H. Ionospheric single-station TEC short-term forecast using RBF neural network[J]. Radio Science, 2014, 49(4): 283-292.
[28] Huang Z, Li Q B, Yuan H. Forecasting of ionospheric vertical TEC 1-h ahead using a genetic algorithm and neural network[J]. Advances in Space Research, 2015, 55(7): 1775-1783.
[29] Feng J, Wang Z, Jiang W, et al. A single-station empirical model for TEC over the Antarctic Peninsula using GPS-TEC data[J]. Radio Science, 2017, 52(2): 196-214.
[30] Orús R, Hernández-Pajares M, Juan J M, et al. Improvement of global ionospheric VTEC maps by using kriging interpolation technique[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2005, 67(16): 1598-1609.
[31] Jakowski N, Hoque M M, Mayer C. A new global TEC model for estimating transionospheric radio wave propagation errors[J]. Journal of Geodesy, 2011, 85: 965-974.
[32] Mukhtarov P, Pancheva D, Andonov B, et al. Global TEC maps based on GNNS data: 2. Model evaluation[J]. Journal of Geophysical Research: Space Physics, 2013, 118(7): 4609-4617.
[33] Wan W X, Ding F, Ren Z P, et al. Modeling the global ionospheric total electron content with empirical orthogonal function analysis[J]. Science China Technological Sciences, 2012, 55: 1161-1168.
[34] Wang C, Xue K, Wang Z, et al. Global ionospheric maps forecasting based on an adaptive autoregressive modeling of grid point VTEC values[J]. Astrophysics and Space Science, 2020, 365: 1-12.
[35] Cherrier N, Castaings T, Boulch A. Deep sequence-to-sequence neural networks for ionospheric activity map prediction[C]. Neural Information Processing: 24th International Conference, ICONIP 2017, Guangzhou, China, November 14—18, 2017, Proceedings, Part V 24. Springer International Publishing, 2017: 545-555.
[36] Feng J, Zhang T, Li W, et al. A new global TEC empirical model based on fusing multi-source data[J]. GPS Solutions, 2023, 27(1): 20.
[37] Mannucci A J, Wilson B D, Yuan D N, et al. A global mapping technique for GPS-derived ionospheric total electron content measurements[J]. Radio Science, 1998, 33(3): 565-582.
[38] Li Z, Yuan Y, Li H, et al. Two-step method for the determination of the differential code biases of COMPASS satellites[J]. Journal of Geodesy, 2012, 86: 1059-1076.
[39] Yuan Y, Li Z, Wang N, et al. Monitoring the ionosphere based on the crustal movement observation network of China[J]. Geodesy and Geodynamics, 2015, 6(2): 73-80.
[40] Hernández-Pajares M, Juan J M, Sanz J, et al. The ionosphere: effects, GPS modeling and the benefits for space geodetic techniques[J]. Journal of Geodesy, 2011, 85(12): 887-907.
[41] Hernández-Pajares M, García-Rigo A, Juan J M, et al. GNSS measurement of EUV photons flux rate during strong and mid solar flares[J]. Space Weather, 2012, 10(12): S12001.
[42] Afraimovich E L, Altyntsev A T, Kosogorov E A, et al. Ionospheric effects of the solar flares of September 23, 1998 and July 29, 1999 as deduced from global GPS network data[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2001, 63(17): 1841-1849.
[43] Xiong B, Wan W, Ning B, et al. A statistic study of ionospheric solar flare activity indicator[J]. Space Weather, 2014, 12(1): 29-40.
[44] Xiong B, Wan W, Yu Y, et al. Investigation of ionospheric TEC over China based on GNSS data[J]. Advances in Space Research, 2016, 58(6): 867-877.
[45] Milan S E, Clausen L B N, Coxon J C, et al. Overview of solar wind-magnetosphere-ionosphere-atmosphere coupling and the generation of magnetospheric currents[J]. Space Science Reviews, 2017, 206(1-4): 547-573.
[46] Gonzalez W D, Joselyn J A, Kamide Y, et al. What is a geomagnetic storm?[J]. Journal of Geophysical Research: Space Physics, 1994, 99(A4): 5771-5792.
[47] Lin C H, Liu J Y, Cheng C Z, et al. Three-dimensional ionospheric electron density structure of the weddell sea anomaly[J]. Journal of Geophysical Research: Space Physics, 2009, 114(A2): A02312.
[48] Zhao L X, Zhang Q H, Xu T, et al. A statistical study of nighttime ionospheric NmF2 enhancement at middle-to-high latitudes in the northern hemisphere[J]. Journal of Geophysical Research: Space Physics, 2022, 127(11): e2022JA030844.